This page is a curated collection of Jupyter/IPython notebooks that are notable for some reason. Feel free to add new content here, but please try to only include links to notebooks that include interesting visual or technical content; this should not simply be a dump of a Google search on every ipynb file out there.

Important contribution instructions: If you add new content, please ensure that for any notebook you link to, the link is to the rendered version using nbviewer, rather than the raw file. Simply paste the notebook URL in the nbviewer box and copy the resulting URL of the rendered version. This will make it much easier for visitors to be able to immediately access the new content.

Note that Matt Davis has conveniently written a set of bookmarklets and extensions to make it a one-click affair to load a Notebook URL into your browser of choice, directly opening into nbviewer.

Build a very simple web browser whose native document format is Markdown.

This is not a Markdown editor inside a conventional web app.

It is a browser for a simpler web.

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

🎮 Run LLMs on your Intel Mac's AMD GPU — llama.cpp + Vulkan/MoltenVK on macOS (Radeon Pro 5500M, 4× faster than CPU). Confirmed working !.

TL;DR: Yes, GPU-accelerated llama.cpp works under macOS on Intel MacBook Pro with AMD Radeon — via Vulkan/MoltenVK. But two flags are mandatory: -fa 0 (RDNA correctness bug) and -c 4096 (VRAM oversubscription trap). Without the second one you get a soul-crushing 0.6 tokens/sec and will wrongly blame the driver. With both: 46–54 t/s generation — ~4× faster than CPU.

Companion guide for Linux/ROCm on the same hardware (zero-fuss option): Ollama + ROCm on T2 MacBook Pro

an improved version of the script from https://gist.github.com/Davr1 important change: GIFs from Discord CDN will be minted so you can access them properly for use with the bulk downloader.

How to use these scripts:

1. Open a browser and log into your discord account.
2. Open devtools using Ctrl+Shift+I (or Cmd ⌘ + Option ⌥ + I)
3. Go to the console tab.
4. Copy and paste the javascript code into the console and hit enter. You might also need to type "allow pasting" if this is your first time using snippets.
5. Download and run the python file in the same directory that the json export is in to bulk download your gif files to your computer.

You are an interactive CLI tool that helps users with software engineering tasks.

Write all English in ASD-STE100 Simplified Technical English. STE is a controlled language. The aerospace industry built it so that a reader who cannot ask a follow-up

How to use these scripts:

1. Open a browser and log into your discord account.
2. Open devtools using Ctrl+Shift+I (or Cmd ⌘ + Option ⌥ + I)
3. Go to the console tab.
4. Paste the snippet and hit enter. You might also need to type "allow pasting" if this is your first time using snippets.

This snippet will export your favourite gifs into a file named discord-favorite-gifs.json.

WIP

This is based on 0 to LSP : Neovim RC From Scratch by ThePrimeagen. Go check it out, big like and subscribe!

This is a learning by doing hands-on writeup. I suggest doing a writeup on your own if you have the time for it.